Overview

Dataset statistics

Number of variables41
Number of observations801
Missing cells522
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.0 KiB
Average record size in memory166.2 B

Variable types

Text4
Categorical17
Numeric20

Alerts

against_electric is highly overall correlated with type1 and 1 other fieldsHigh correlation
against_fight is highly overall correlated with against_normal and 1 other fieldsHigh correlation
against_ground is highly overall correlated with against_flying and 2 other fieldsHigh correlation
against_poison is highly overall correlated with against_dragon and 2 other fieldsHigh correlation
against_psychic is highly overall correlated with against_ghost and 2 other fieldsHigh correlation
attack is highly overall correlated with base_total and 4 other fieldsHigh correlation
base_egg_steps is highly overall correlated with is_legendaryHigh correlation
base_happiness is highly overall correlated with is_legendaryHigh correlation
base_total is highly overall correlated with attack and 8 other fieldsHigh correlation
defense is highly overall correlated with attack and 3 other fieldsHigh correlation
experience_growth is highly overall correlated with is_legendaryHigh correlation
height_m is highly overall correlated with attack and 4 other fieldsHigh correlation
hp is highly overall correlated with attack and 3 other fieldsHigh correlation
pokedex_number is highly overall correlated with generationHigh correlation
sp_attack is highly overall correlated with base_total and 1 other fieldsHigh correlation
sp_defense is highly overall correlated with base_total and 3 other fieldsHigh correlation
speed is highly overall correlated with base_totalHigh correlation
weight_kg is highly overall correlated with attack and 4 other fieldsHigh correlation
generation is highly overall correlated with pokedex_numberHigh correlation
against_dark is highly overall correlated with against_ghost and 2 other fieldsHigh correlation
against_dragon is highly overall correlated with against_poison and 2 other fieldsHigh correlation
against_fairy is highly overall correlated with type1High correlation
against_fire is highly overall correlated with type1High correlation
against_flying is highly overall correlated with against_ground and 2 other fieldsHigh correlation
against_ghost is highly overall correlated with against_psychic and 2 other fieldsHigh correlation
against_grass is highly overall correlated with type1High correlation
against_ice is highly overall correlated with type1High correlation
against_normal is highly overall correlated with against_fight and 3 other fieldsHigh correlation
against_steel is highly overall correlated with type1High correlation
against_water is highly overall correlated with type1High correlation
capture_rate is highly overall correlated with is_legendaryHigh correlation
type1 is highly overall correlated with against_electric and 15 other fieldsHigh correlation
type2 is highly overall correlated with against_electric and 6 other fieldsHigh correlation
is_legendary is highly overall correlated with base_egg_steps and 4 other fieldsHigh correlation
against_dragon is highly imbalanced (54.7%)Imbalance
against_normal is highly imbalanced (57.4%)Imbalance
is_legendary is highly imbalanced (57.2%)Imbalance
height_m has 20 (2.5%) missing valuesMissing
percentage_male has 98 (12.2%) missing valuesMissing
type2 has 384 (47.9%) missing valuesMissing
weight_kg has 20 (2.5%) missing valuesMissing
pokedex_number is uniformly distributedUniform
japanese_name has unique valuesUnique
name has unique valuesUnique
pokedex_number has unique valuesUnique
against_electric has 64 (8.0%) zerosZeros
against_fight has 41 (5.1%) zerosZeros
against_ground has 98 (12.2%) zerosZeros
against_poison has 46 (5.7%) zerosZeros
against_psychic has 46 (5.7%) zerosZeros
base_happiness has 36 (4.5%) zerosZeros
percentage_male has 27 (3.4%) zerosZeros

Reproduction

Analysis started2023-09-20 10:13:46.112361
Analysis finished2023-09-20 10:14:27.034861
Duration40.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct482
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-09-20T15:44:27.186446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length89
Median length48
Mean length32.378277
Min length9

Characters and Unicode

Total characters25935
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)32.6%

Sample

1st row['Overgrow', 'Chlorophyll']
2nd row['Overgrow', 'Chlorophyll']
3rd row['Overgrow', 'Chlorophyll']
4th row['Blaze', 'Solar Power']
5th row['Blaze', 'Solar Power']
ValueCountFrequency (%)
armor 53
 
1.8%
body 51
 
1.7%
sand 51
 
1.7%
veil 48
 
1.6%
guard 47
 
1.6%
sturdy 41
 
1.4%
force 41
 
1.4%
swift 38
 
1.3%
swim 38
 
1.3%
water 38
 
1.3%
Other values (286) 2526
85.0%
2023-09-20T15:44:27.479108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 3972
 
15.3%
2171
 
8.4%
e 1716
 
6.6%
r 1386
 
5.3%
, 1185
 
4.6%
a 1133
 
4.4%
t 1112
 
4.3%
i 1101
 
4.2%
o 1095
 
4.2%
n 976
 
3.8%
Other values (46) 10088
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14036
54.1%
Other Punctuation 5157
 
19.9%
Uppercase Letter 2968
 
11.4%
Space Separator 2171
 
8.4%
Open Punctuation 801
 
3.1%
Close Punctuation 801
 
3.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1716
12.2%
r 1386
 
9.9%
a 1133
 
8.1%
t 1112
 
7.9%
i 1101
 
7.8%
o 1095
 
7.8%
n 976
 
7.0%
l 753
 
5.4%
u 579
 
4.1%
d 512
 
3.6%
Other values (16) 3673
26.2%
Uppercase Letter
ValueCountFrequency (%)
S 599
20.2%
F 227
 
7.6%
C 178
 
6.0%
P 176
 
5.9%
B 173
 
5.8%
A 163
 
5.5%
T 156
 
5.3%
R 148
 
5.0%
I 142
 
4.8%
L 126
 
4.2%
Other values (14) 880
29.6%
Other Punctuation
ValueCountFrequency (%)
' 3972
77.0%
, 1185
 
23.0%
Space Separator
ValueCountFrequency (%)
2171
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 801
100.0%
Close Punctuation
ValueCountFrequency (%)
] 801
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17004
65.6%
Common 8931
34.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1716
 
10.1%
r 1386
 
8.2%
a 1133
 
6.7%
t 1112
 
6.5%
i 1101
 
6.5%
o 1095
 
6.4%
n 976
 
5.7%
l 753
 
4.4%
S 599
 
3.5%
u 579
 
3.4%
Other values (40) 6554
38.5%
Common
ValueCountFrequency (%)
' 3972
44.5%
2171
24.3%
, 1185
 
13.3%
[ 801
 
9.0%
] 801
 
9.0%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 3972
 
15.3%
2171
 
8.4%
e 1716
 
6.6%
r 1386
 
5.3%
, 1185
 
4.6%
a 1133
 
4.4%
t 1112
 
4.3%
i 1101
 
4.2%
o 1095
 
4.2%
n 976
 
3.8%
Other values (46) 10088
38.9%

against_bug
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
376 
0.5
247 
2.0
128 
0.25
42 
4.0
 
8

Length

Max length4
Median length3
Mean length3.0524345
Min length3

Characters and Unicode

Total characters2445
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 376
46.9%
0.5 247
30.8%
2.0 128
 
16.0%
0.25 42
 
5.2%
4.0 8
 
1.0%

Length

2023-09-20T15:44:27.570757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:27.658041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 376
46.9%
0.5 247
30.8%
2.0 128
 
16.0%
0.25 42
 
5.2%
4.0 8
 
1.0%

Most occurring characters

ValueCountFrequency (%)
. 801
32.8%
0 801
32.8%
1 376
15.4%
5 289
 
11.8%
2 170
 
7.0%
4 8
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1644
67.2%
Other Punctuation 801
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
48.7%
1 376
22.9%
5 289
 
17.6%
2 170
 
10.3%
4 8
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2445
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
32.8%
0 801
32.8%
1 376
15.4%
5 289
 
11.8%
2 170
 
7.0%
4 8
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
32.8%
0 801
32.8%
1 376
15.4%
5 289
 
11.8%
2 170
 
7.0%
4 8
 
0.3%

against_dark
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
565 
0.5
126 
2.0
105 
0.25
 
3
4.0
 
2

Length

Max length4
Median length3
Mean length3.0037453
Min length3

Characters and Unicode

Total characters2406
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 565
70.5%
0.5 126
 
15.7%
2.0 105
 
13.1%
0.25 3
 
0.4%
4.0 2
 
0.2%

Length

2023-09-20T15:44:27.731564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:27.807451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 565
70.5%
0.5 126
 
15.7%
2.0 105
 
13.1%
0.25 3
 
0.4%
4.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 565
23.5%
5 129
 
5.4%
2 108
 
4.5%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1605
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.9%
1 565
35.2%
5 129
 
8.0%
2 108
 
6.7%
4 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 565
23.5%
5 129
 
5.4%
2 108
 
4.5%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 565
23.5%
5 129
 
5.4%
2 108
 
4.5%
4 2
 
0.1%

against_dragon
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
669 
0.0
 
47
2.0
 
43
0.5
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2403
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 669
83.5%
0.0 47
 
5.9%
2.0 43
 
5.4%
0.5 42
 
5.2%

Length

2023-09-20T15:44:27.878778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:27.951329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 669
83.5%
0.0 47
 
5.9%
2.0 43
 
5.4%
0.5 42
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 848
35.3%
. 801
33.3%
1 669
27.8%
2 43
 
1.8%
5 42
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1602
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 848
52.9%
1 669
41.8%
2 43
 
2.7%
5 42
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 848
35.3%
. 801
33.3%
1 669
27.8%
2 43
 
1.8%
5 42
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 848
35.3%
. 801
33.3%
1 669
27.8%
2 43
 
1.8%
5 42
 
1.7%

against_electric
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.07397
Minimum0
Maximum4
Zeros64
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:28.012897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.65496159
Coefficient of variation (CV)0.60985089
Kurtosis2.1131568
Mean1.07397
Median Absolute Deviation (MAD)0.5
Skewness0.93483949
Sum860.25
Variance0.42897472
MonotonicityNot monotonic
2023-09-20T15:44:28.070594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 392
48.9%
2 181
22.6%
0.5 156
 
19.5%
0 64
 
8.0%
4 7
 
0.9%
0.25 1
 
0.1%
ValueCountFrequency (%)
0 64
 
8.0%
0.25 1
 
0.1%
0.5 156
 
19.5%
1 392
48.9%
2 181
22.6%
4 7
 
0.9%
ValueCountFrequency (%)
4 7
 
0.9%
2 181
22.6%
1 392
48.9%
0.5 156
 
19.5%
0.25 1
 
0.1%
0 64
 
8.0%

against_fairy
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
541 
0.5
145 
2.0
103 
4.0
 
9
0.25
 
3

Length

Max length4
Median length3
Mean length3.0037453
Min length3

Characters and Unicode

Total characters2406
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 541
67.5%
0.5 145
 
18.1%
2.0 103
 
12.9%
4.0 9
 
1.1%
0.25 3
 
0.4%

Length

2023-09-20T15:44:28.140342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:28.218021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 541
67.5%
0.5 145
 
18.1%
2.0 103
 
12.9%
4.0 9
 
1.1%
0.25 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 541
22.5%
5 148
 
6.2%
2 106
 
4.4%
4 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1605
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.9%
1 541
33.7%
5 148
 
9.2%
2 106
 
6.6%
4 9
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 541
22.5%
5 148
 
6.2%
2 106
 
4.4%
4 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 541
22.5%
5 148
 
6.2%
2 106
 
4.4%
4 9
 
0.4%

against_fight
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0655431
Minimum0
Maximum4
Zeros41
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:28.288990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.71725082
Coefficient of variation (CV)0.6731317
Kurtosis2.6815526
Mean1.0655431
Median Absolute Deviation (MAD)0.5
Skewness1.2571148
Sum853.5
Variance0.51444876
MonotonicityNot monotonic
2023-09-20T15:44:28.349796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 326
40.7%
0.5 193
24.1%
2 184
23.0%
0.25 44
 
5.5%
0 41
 
5.1%
4 13
 
1.6%
ValueCountFrequency (%)
0 41
 
5.1%
0.25 44
 
5.5%
0.5 193
24.1%
1 326
40.7%
2 184
23.0%
4 13
 
1.6%
ValueCountFrequency (%)
4 13
 
1.6%
2 184
23.0%
1 326
40.7%
0.5 193
24.1%
0.25 44
 
5.5%
0 41
 
5.1%

against_fire
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
354 
0.5
226 
2.0
187 
0.25
 
18
4.0
 
16

Length

Max length4
Median length3
Mean length3.0224719
Min length3

Characters and Unicode

Total characters2421
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 354
44.2%
0.5 226
28.2%
2.0 187
23.3%
0.25 18
 
2.2%
4.0 16
 
2.0%

Length

2023-09-20T15:44:28.423691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:28.503632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 354
44.2%
0.5 226
28.2%
2.0 187
23.3%
0.25 18
 
2.2%
4.0 16
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 801
33.1%
0 801
33.1%
1 354
14.6%
5 244
 
10.1%
2 205
 
8.5%
4 16
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1620
66.9%
Other Punctuation 801
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.4%
1 354
21.9%
5 244
 
15.1%
2 205
 
12.7%
4 16
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2421
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.1%
0 801
33.1%
1 354
14.6%
5 244
 
10.1%
2 205
 
8.5%
4 16
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.1%
0 801
33.1%
1 354
14.6%
5 244
 
10.1%
2 205
 
8.5%
4 16
 
0.7%

against_flying
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
488 
2.0
181 
0.5
110 
4.0
 
12
0.25
 
10

Length

Max length4
Median length3
Mean length3.0124844
Min length3

Characters and Unicode

Total characters2413
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 488
60.9%
2.0 181
 
22.6%
0.5 110
 
13.7%
4.0 12
 
1.5%
0.25 10
 
1.2%

Length

2023-09-20T15:44:28.579983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:28.663994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 488
60.9%
2.0 181
 
22.6%
0.5 110
 
13.7%
4.0 12
 
1.5%
0.25 10
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 488
20.2%
2 191
 
7.9%
5 120
 
5.0%
4 12
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1612
66.8%
Other Punctuation 801
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.7%
1 488
30.3%
2 191
 
11.8%
5 120
 
7.4%
4 12
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 488
20.2%
2 191
 
7.9%
5 120
 
5.0%
4 12
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 488
20.2%
2 191
 
7.9%
5 120
 
5.0%
4 12
 
0.5%

against_ghost
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
536 
2.0
112 
0.0
109 
0.5
 
42
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2403
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 536
66.9%
2.0 112
 
14.0%
0.0 109
 
13.6%
0.5 42
 
5.2%
4.0 2
 
0.2%

Length

2023-09-20T15:44:28.739268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:28.818865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 536
66.9%
2.0 112
 
14.0%
0.0 109
 
13.6%
0.5 42
 
5.2%
4.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 910
37.9%
. 801
33.3%
1 536
22.3%
2 112
 
4.7%
5 42
 
1.7%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1602
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 910
56.8%
1 536
33.5%
2 112
 
7.0%
5 42
 
2.6%
4 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 910
37.9%
. 801
33.3%
1 536
22.3%
2 112
 
4.7%
5 42
 
1.7%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 910
37.9%
. 801
33.3%
1 536
22.3%
2 112
 
4.7%
5 42
 
1.7%
4 2
 
0.1%

against_grass
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
297 
0.5
256 
2.0
135 
0.25
85 
4.0
 
28

Length

Max length4
Median length3
Mean length3.1061174
Min length3

Characters and Unicode

Total characters2488
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.25
2nd row0.25
3rd row0.25
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 297
37.1%
0.5 256
32.0%
2.0 135
16.9%
0.25 85
 
10.6%
4.0 28
 
3.5%

Length

2023-09-20T15:44:28.901301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:28.989665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 297
37.1%
0.5 256
32.0%
2.0 135
16.9%
0.25 85
 
10.6%
4.0 28
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 801
32.2%
0 801
32.2%
5 341
13.7%
1 297
 
11.9%
2 220
 
8.8%
4 28
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1687
67.8%
Other Punctuation 801
32.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
47.5%
5 341
20.2%
1 297
 
17.6%
2 220
 
13.0%
4 28
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
32.2%
0 801
32.2%
5 341
13.7%
1 297
 
11.9%
2 220
 
8.8%
4 28
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
32.2%
0 801
32.2%
5 341
13.7%
1 297
 
11.9%
2 220
 
8.8%
4 28
 
1.1%

against_ground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0980025
Minimum0
Maximum4
Zeros98
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:29.063761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73881817
Coefficient of variation (CV)0.67287476
Kurtosis2.66751
Mean1.0980025
Median Absolute Deviation (MAD)0
Skewness1.0792491
Sum879.5
Variance0.5458523
MonotonicityNot monotonic
2023-09-20T15:44:29.122339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 402
50.2%
2 184
23.0%
0 98
 
12.2%
0.5 96
 
12.0%
4 15
 
1.9%
0.25 6
 
0.7%
ValueCountFrequency (%)
0 98
 
12.2%
0.25 6
 
0.7%
0.5 96
 
12.0%
1 402
50.2%
2 184
23.0%
4 15
 
1.9%
ValueCountFrequency (%)
4 15
 
1.9%
2 184
23.0%
1 402
50.2%
0.5 96
 
12.0%
0.25 6
 
0.7%
0 98
 
12.2%

against_ice
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
349 
2.0
212 
0.5
209 
4.0
 
22
0.25
 
9

Length

Max length4
Median length3
Mean length3.011236
Min length3

Characters and Unicode

Total characters2412
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 349
43.6%
2.0 212
26.5%
0.5 209
26.1%
4.0 22
 
2.7%
0.25 9
 
1.1%

Length

2023-09-20T15:44:29.196892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:29.278035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 349
43.6%
2.0 212
26.5%
0.5 209
26.1%
4.0 22
 
2.7%
0.25 9
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 349
14.5%
2 221
 
9.2%
5 218
 
9.0%
4 22
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1611
66.8%
Other Punctuation 801
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.7%
1 349
21.7%
2 221
 
13.7%
5 218
 
13.5%
4 22
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 349
14.5%
2 221
 
9.2%
5 218
 
9.0%
4 22
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 349
14.5%
2 221
 
9.2%
5 218
 
9.0%
4 22
 
0.9%

against_normal
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
664 
0.5
90 
0.0
 
41
0.25
 
6

Length

Max length4
Median length3
Mean length3.0074906
Min length3

Characters and Unicode

Total characters2409
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 664
82.9%
0.5 90
 
11.2%
0.0 41
 
5.1%
0.25 6
 
0.7%

Length

2023-09-20T15:44:29.352230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:29.427206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 664
82.9%
0.5 90
 
11.2%
0.0 41
 
5.1%
0.25 6
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 842
35.0%
. 801
33.3%
1 664
27.6%
5 96
 
4.0%
2 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1608
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 842
52.4%
1 664
41.3%
5 96
 
6.0%
2 6
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2409
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 842
35.0%
. 801
33.3%
1 664
27.6%
5 96
 
4.0%
2 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 842
35.0%
. 801
33.3%
1 664
27.6%
5 96
 
4.0%
2 6
 
0.2%

against_poison
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97534332
Minimum0
Maximum4
Zeros46
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:29.490767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.54937524
Coefficient of variation (CV)0.56326344
Kurtosis5.0853481
Mean0.97534332
Median Absolute Deviation (MAD)0
Skewness1.3734683
Sum781.25
Variance0.30181316
MonotonicityNot monotonic
2023-09-20T15:44:29.549806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 476
59.4%
0.5 153
 
19.1%
2 102
 
12.7%
0 46
 
5.7%
0.25 19
 
2.4%
4 5
 
0.6%
ValueCountFrequency (%)
0 46
 
5.7%
0.25 19
 
2.4%
0.5 153
 
19.1%
1 476
59.4%
2 102
 
12.7%
4 5
 
0.6%
ValueCountFrequency (%)
4 5
 
0.6%
2 102
 
12.7%
1 476
59.4%
0.5 153
 
19.1%
0.25 19
 
2.4%
0 46
 
5.7%

against_psychic
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0053059
Minimum0
Maximum4
Zeros46
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:29.614623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49518296
Coefficient of variation (CV)0.49256945
Kurtosis3.6248195
Mean1.0053059
Median Absolute Deviation (MAD)0
Skewness0.93708932
Sum805.25
Variance0.24520618
MonotonicityNot monotonic
2023-09-20T15:44:29.673682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 539
67.3%
0.5 105
 
13.1%
2 102
 
12.7%
0 46
 
5.7%
0.25 7
 
0.9%
4 2
 
0.2%
ValueCountFrequency (%)
0 46
 
5.7%
0.25 7
 
0.9%
0.5 105
 
13.1%
1 539
67.3%
2 102
 
12.7%
4 2
 
0.2%
ValueCountFrequency (%)
4 2
 
0.2%
2 102
 
12.7%
1 539
67.3%
0.5 105
 
13.1%
0.25 7
 
0.9%
0 46
 
5.7%

against_rock
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
449 
2.0
198 
0.5
127 
4.0
 
23
0.25
 
4

Length

Max length4
Median length3
Mean length3.0049938
Min length3

Characters and Unicode

Total characters2407
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 449
56.1%
2.0 198
24.7%
0.5 127
 
15.9%
4.0 23
 
2.9%
0.25 4
 
0.5%

Length

2023-09-20T15:44:29.743956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:29.821757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 449
56.1%
2.0 198
24.7%
0.5 127
 
15.9%
4.0 23
 
2.9%
0.25 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 449
18.7%
2 202
 
8.4%
5 131
 
5.4%
4 23
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1606
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.9%
1 449
28.0%
2 202
 
12.6%
5 131
 
8.2%
4 23
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2407
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 449
18.7%
2 202
 
8.4%
5 131
 
5.4%
4 23
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 449
18.7%
2 202
 
8.4%
5 131
 
5.4%
4 23
 
1.0%

against_steel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
451 
0.5
237 
2.0
100 
0.25
 
9
4.0
 
4

Length

Max length4
Median length3
Mean length3.011236
Min length3

Characters and Unicode

Total characters2412
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
1.0 451
56.3%
0.5 237
29.6%
2.0 100
 
12.5%
0.25 9
 
1.1%
4.0 4
 
0.5%

Length

2023-09-20T15:44:29.896677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:29.974435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 451
56.3%
0.5 237
29.6%
2.0 100
 
12.5%
0.25 9
 
1.1%
4.0 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 451
18.7%
5 246
 
10.2%
2 109
 
4.5%
4 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1611
66.8%
Other Punctuation 801
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.7%
1 451
28.0%
5 246
 
15.3%
2 109
 
6.8%
4 4
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 451
18.7%
5 246
 
10.2%
2 109
 
4.5%
4 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.2%
0 801
33.2%
1 451
18.7%
5 246
 
10.2%
2 109
 
4.5%
4 4
 
0.2%

against_water
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
426 
0.5
229 
2.0
129 
4.0
 
12
0.25
 
5

Length

Max length4
Median length3
Mean length3.0062422
Min length3

Characters and Unicode

Total characters2408
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 426
53.2%
0.5 229
28.6%
2.0 129
 
16.1%
4.0 12
 
1.5%
0.25 5
 
0.6%

Length

2023-09-20T15:44:30.047726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:30.128743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 426
53.2%
0.5 229
28.6%
2.0 129
 
16.1%
4.0 12
 
1.5%
0.25 5
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 426
17.7%
5 234
 
9.7%
2 134
 
5.6%
4 12
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1607
66.7%
Other Punctuation 801
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 801
49.8%
1 426
26.5%
5 234
 
14.6%
2 134
 
8.3%
4 12
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 426
17.7%
5 234
 
9.7%
2 134
 
5.6%
4 12
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 801
33.3%
0 801
33.3%
1 426
17.7%
5 234
 
9.7%
2 134
 
5.6%
4 12
 
0.5%

attack
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.857678
Minimum5
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:30.215666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q3100
95-th percentile135
Maximum185
Range180
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.15882
Coefficient of variation (CV)0.41304623
Kurtosis0.071336832
Mean77.857678
Median Absolute Deviation (MAD)22
Skewness0.53081074
Sum62364
Variance1034.1897
MonotonicityNot monotonic
2023-09-20T15:44:30.304661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 38
 
4.7%
60 35
 
4.4%
65 35
 
4.4%
55 35
 
4.4%
75 34
 
4.2%
50 34
 
4.2%
80 33
 
4.1%
70 32
 
4.0%
85 32
 
4.0%
90 27
 
3.4%
Other values (104) 466
58.2%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 1
 
0.1%
20 8
1.0%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 7
0.9%
27 1
 
0.1%
29 3
 
0.4%
ValueCountFrequency (%)
185 1
 
0.1%
181 1
 
0.1%
180 2
 
0.2%
170 1
 
0.1%
165 3
 
0.4%
164 1
 
0.1%
160 5
0.6%
155 2
 
0.2%
150 8
1.0%
147 1
 
0.1%

base_egg_steps
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7191.0112
Minimum1280
Maximum30720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:30.379758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile3840
Q15120
median5120
Q36400
95-th percentile30720
Maximum30720
Range29440
Interquartile range (IQR)1280

Descriptive statistics

Standard deviation6558.2204
Coefficient of variation (CV)0.91200253
Kurtosis7.5813032
Mean7191.0112
Median Absolute Deviation (MAD)0
Skewness2.9557545
Sum5760000
Variance43010255
MonotonicityNot monotonic
2023-09-20T15:44:30.438772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5120 436
54.4%
3840 140
 
17.5%
6400 55
 
6.9%
30720 49
 
6.1%
10240 42
 
5.2%
7680 26
 
3.2%
2560 22
 
2.7%
20480 16
 
2.0%
8960 13
 
1.6%
1280 2
 
0.2%
ValueCountFrequency (%)
1280 2
 
0.2%
2560 22
 
2.7%
3840 140
 
17.5%
5120 436
54.4%
6400 55
 
6.9%
7680 26
 
3.2%
8960 13
 
1.6%
10240 42
 
5.2%
20480 16
 
2.0%
30720 49
 
6.1%
ValueCountFrequency (%)
30720 49
 
6.1%
20480 16
 
2.0%
10240 42
 
5.2%
8960 13
 
1.6%
7680 26
 
3.2%
6400 55
 
6.9%
5120 436
54.4%
3840 140
 
17.5%
2560 22
 
2.7%
1280 2
 
0.2%

base_happiness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.362047
Minimum0
Maximum140
Zeros36
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:30.499029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q170
median70
Q370
95-th percentile70
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.598948
Coefficient of variation (CV)0.29985211
Kurtosis5.9369893
Mean65.362047
Median Absolute Deviation (MAD)0
Skewness-1.182299
Sum52355
Variance384.11876
MonotonicityNot monotonic
2023-09-20T15:44:30.556577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
70 667
83.3%
35 69
 
8.6%
0 36
 
4.5%
100 14
 
1.7%
140 10
 
1.2%
90 5
 
0.6%
ValueCountFrequency (%)
0 36
 
4.5%
35 69
 
8.6%
70 667
83.3%
90 5
 
0.6%
100 14
 
1.7%
140 10
 
1.2%
ValueCountFrequency (%)
140 10
 
1.2%
100 14
 
1.7%
90 5
 
0.6%
70 667
83.3%
35 69
 
8.6%
0 36
 
4.5%

base_total
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.37703
Minimum180
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:30.638179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1320
median435
Q3505
95-th percentile618
Maximum780
Range600
Interquartile range (IQR)185

Descriptive statistics

Standard deviation119.20358
Coefficient of variation (CV)0.2782679
Kurtosis-0.52795798
Mean428.37703
Median Absolute Deviation (MAD)94
Skewness0.17459276
Sum343130
Variance14209.493
MonotonicityNot monotonic
2023-09-20T15:44:30.727333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405 26
 
3.2%
600 26
 
3.2%
500 21
 
2.6%
300 21
 
2.6%
580 20
 
2.5%
490 18
 
2.2%
485 16
 
2.0%
495 16
 
2.0%
480 15
 
1.9%
330 15
 
1.9%
Other values (193) 607
75.8%
ValueCountFrequency (%)
180 1
 
0.1%
190 1
 
0.1%
194 1
 
0.1%
195 3
0.4%
198 1
 
0.1%
200 4
0.5%
205 5
0.6%
210 4
0.5%
213 1
 
0.1%
215 1
 
0.1%
ValueCountFrequency (%)
780 2
 
0.2%
770 2
 
0.2%
720 1
 
0.1%
708 1
 
0.1%
700 8
1.0%
680 12
1.5%
670 2
 
0.2%
640 2
 
0.2%
635 1
 
0.1%
634 1
 
0.1%

capture_rate
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
45
250 
190
75 
255
69 
75
61 
3
58 
Other values (29)
288 

Length

Max length24
Median length2
Mean length2.3146067
Min length1

Characters and Unicode

Total characters1854
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st row45
2nd row45
3rd row45
4th row45
5th row45

Common Values

ValueCountFrequency (%)
45 250
31.2%
190 75
 
9.4%
255 69
 
8.6%
75 61
 
7.6%
3 58
 
7.2%
120 55
 
6.9%
60 50
 
6.2%
90 38
 
4.7%
30 20
 
2.5%
200 19
 
2.4%
Other values (24) 106
13.2%

Length

2023-09-20T15:44:30.808473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45 250
31.1%
190 75
 
9.3%
255 69
 
8.6%
75 61
 
7.6%
3 58
 
7.2%
120 55
 
6.8%
60 50
 
6.2%
90 38
 
4.7%
30 21
 
2.6%
200 19
 
2.4%
Other values (25) 107
13.3%

Most occurring characters

ValueCountFrequency (%)
5 519
28.0%
0 334
18.0%
4 257
13.9%
2 207
 
11.2%
1 177
 
9.5%
9 113
 
6.1%
3 89
 
4.8%
7 72
 
3.9%
6 54
 
2.9%
8 13
 
0.7%
Other values (10) 19
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1835
99.0%
Lowercase Letter 11
 
0.6%
Close Punctuation 2
 
0.1%
Space Separator 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 519
28.3%
0 334
18.2%
4 257
14.0%
2 207
 
11.3%
1 177
 
9.6%
9 113
 
6.2%
3 89
 
4.9%
7 72
 
3.9%
6 54
 
2.9%
8 13
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
e 4
36.4%
o 2
18.2%
r 2
18.2%
t 2
18.2%
i 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
C 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1841
99.3%
Latin 13
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 519
28.2%
0 334
18.1%
4 257
14.0%
2 207
 
11.2%
1 177
 
9.6%
9 113
 
6.1%
3 89
 
4.8%
7 72
 
3.9%
6 54
 
2.9%
8 13
 
0.7%
Other values (3) 6
 
0.3%
Latin
ValueCountFrequency (%)
e 4
30.8%
o 2
15.4%
r 2
15.4%
t 2
15.4%
M 1
 
7.7%
i 1
 
7.7%
C 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 519
28.0%
0 334
18.0%
4 257
13.9%
2 207
 
11.2%
1 177
 
9.5%
9 113
 
6.1%
3 89
 
4.8%
7 72
 
3.9%
6 54
 
2.9%
8 13
 
0.7%
Other values (10) 19
 
1.0%
Distinct588
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-09-20T15:44:31.021335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length51
Median length19
Mean length15.626717
Min length11

Characters and Unicode

Total characters12517
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)54.9%

Sample

1st rowSeed Pokémon
2nd rowSeed Pokémon
3rd rowSeed Pokémon
4th rowLizard Pokémon
5th rowFlame Pokémon
ValueCountFrequency (%)
pokémon 801
43.3%
sea 15
 
0.8%
bird 14
 
0.8%
iron 10
 
0.5%
poison 10
 
0.5%
mouse 9
 
0.5%
tiny 9
 
0.5%
dragon 8
 
0.4%
water 8
 
0.4%
fish 8
 
0.4%
Other values (595) 956
51.7%
2023-09-20T15:44:31.339791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2032
16.2%
n 1158
 
9.3%
1047
 
8.4%
m 889
 
7.1%
k 881
 
7.0%
P 871
 
7.0%
é 802
 
6.4%
e 558
 
4.5%
a 429
 
3.4%
r 423
 
3.4%
Other values (46) 3427
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9599
76.7%
Uppercase Letter 1862
 
14.9%
Space Separator 1047
 
8.4%
Dash Punctuation 5
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2032
21.2%
n 1158
12.1%
m 889
9.3%
k 881
9.2%
é 802
 
8.4%
e 558
 
5.8%
a 429
 
4.5%
r 423
 
4.4%
i 420
 
4.4%
l 330
 
3.4%
Other values (17) 1677
17.5%
Uppercase Letter
ValueCountFrequency (%)
P 871
46.8%
S 162
 
8.7%
B 114
 
6.1%
C 86
 
4.6%
F 82
 
4.4%
M 72
 
3.9%
T 62
 
3.3%
D 54
 
2.9%
L 54
 
2.9%
W 47
 
2.5%
Other values (15) 258
 
13.9%
Space Separator
ValueCountFrequency (%)
1047
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11461
91.6%
Common 1056
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2032
17.7%
n 1158
 
10.1%
m 889
 
7.8%
k 881
 
7.7%
P 871
 
7.6%
é 802
 
7.0%
e 558
 
4.9%
a 429
 
3.7%
r 423
 
3.7%
i 420
 
3.7%
Other values (42) 2998
26.2%
Common
ValueCountFrequency (%)
1047
99.1%
- 5
 
0.5%
( 2
 
0.2%
) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11715
93.6%
None 802
 
6.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2032
17.3%
n 1158
 
9.9%
1047
 
8.9%
m 889
 
7.6%
k 881
 
7.5%
P 871
 
7.4%
e 558
 
4.8%
a 429
 
3.7%
r 423
 
3.6%
i 420
 
3.6%
Other values (45) 3007
25.7%
None
ValueCountFrequency (%)
é 802
100.0%

defense
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.008739
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:31.438815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median70
Q390
95-th percentile130
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation30.769159
Coefficient of variation (CV)0.42144488
Kurtosis2.5833589
Mean73.008739
Median Absolute Deviation (MAD)20
Skewness1.1210583
Sum58480
Variance946.74117
MonotonicityNot monotonic
2023-09-20T15:44:31.527789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 51
 
6.4%
70 49
 
6.1%
60 46
 
5.7%
80 41
 
5.1%
40 39
 
4.9%
90 33
 
4.1%
65 33
 
4.1%
45 32
 
4.0%
55 30
 
3.7%
100 29
 
3.6%
Other values (99) 418
52.2%
ValueCountFrequency (%)
5 2
 
0.2%
10 1
 
0.1%
15 4
 
0.5%
20 3
 
0.4%
23 1
 
0.1%
25 1
 
0.1%
28 1
 
0.1%
30 16
2.0%
31 1
 
0.1%
32 2
 
0.2%
ValueCountFrequency (%)
230 3
0.4%
200 1
 
0.1%
184 1
 
0.1%
180 2
 
0.2%
168 1
 
0.1%
160 2
 
0.2%
152 1
 
0.1%
150 5
0.6%
145 2
 
0.2%
140 6
0.7%

experience_growth
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054995.9
Minimum600000
Maximum1640000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:31.597978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile800000
Q11000000
median1000000
Q31059860
95-th percentile1250000
Maximum1640000
Range1040000
Interquartile range (IQR)59860

Descriptive statistics

Standard deviation160255.84
Coefficient of variation (CV)0.15190186
Kurtosis2.8529077
Mean1054995.9
Median Absolute Deviation (MAD)59860
Skewness0.31112777
Sum8.4505172 × 108
Variance2.5681933 × 1010
MonotonicityNot monotonic
2023-09-20T15:44:31.661866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1000000 335
41.8%
1059860 202
25.2%
1250000 172
21.5%
800000 56
 
7.0%
600000 22
 
2.7%
1640000 14
 
1.7%
ValueCountFrequency (%)
600000 22
 
2.7%
800000 56
 
7.0%
1000000 335
41.8%
1059860 202
25.2%
1250000 172
21.5%
1640000 14
 
1.7%
ValueCountFrequency (%)
1640000 14
 
1.7%
1250000 172
21.5%
1059860 202
25.2%
1000000 335
41.8%
800000 56
 
7.0%
600000 22
 
2.7%

height_m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)6.5%
Missing20
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.1638924
Minimum0.1
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:31.742078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.30000001
Q10.60000002
median1
Q31.5
95-th percentile2.5
Maximum14.5
Range14.4
Interquartile range (IQR)0.89999998

Descriptive statistics

Standard deviation1.0803263
Coefficient of variation (CV)0.92820116
Kurtosis43.104656
Mean1.1638924
Median Absolute Deviation (MAD)0.5
Skewness5.0800157
Sum909
Variance1.1671048
MonotonicityNot monotonic
2023-09-20T15:44:31.830233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6000000238 70
 
8.7%
0.5 60
 
7.5%
0.400000006 59
 
7.4%
1 57
 
7.1%
0.3000000119 53
 
6.6%
1.200000048 49
 
6.1%
0.8000000119 43
 
5.4%
1.5 43
 
5.4%
0.6999999881 37
 
4.6%
1.100000024 36
 
4.5%
Other values (41) 274
34.2%
ValueCountFrequency (%)
0.1000000015 5
 
0.6%
0.200000003 17
 
2.1%
0.3000000119 53
6.6%
0.400000006 59
7.4%
0.5 60
7.5%
0.6000000238 70
8.7%
0.6999999881 37
4.6%
0.8000000119 43
5.4%
0.8999999762 32
4.0%
1 57
7.1%
ValueCountFrequency (%)
14.5 1
0.1%
9.199999809 2
0.2%
8.800000191 1
0.1%
7 1
0.1%
6.5 1
0.1%
6.199999809 1
0.1%
5.800000191 1
0.1%
5.5 1
0.1%
5.400000095 1
0.1%
5.199999809 1
0.1%

hp
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.958801
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:31.922537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.576015
Coefficient of variation (CV)0.38538974
Kurtosis8.3349728
Mean68.958801
Median Absolute Deviation (MAD)15
Skewness1.8265905
Sum55236
Variance706.28455
MonotonicityNot monotonic
2023-09-20T15:44:32.012548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 68
 
8.5%
70 55
 
6.9%
50 54
 
6.7%
75 44
 
5.5%
65 44
 
5.5%
45 42
 
5.2%
40 41
 
5.1%
80 38
 
4.7%
55 37
 
4.6%
100 28
 
3.5%
Other values (89) 350
43.7%
ValueCountFrequency (%)
1 1
 
0.1%
10 1
 
0.1%
20 6
 
0.7%
25 3
 
0.4%
28 1
 
0.1%
30 13
1.6%
31 1
 
0.1%
35 16
2.0%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
255 1
 
0.1%
250 1
 
0.1%
223 1
 
0.1%
216 1
 
0.1%
190 1
 
0.1%
170 1
 
0.1%
165 1
 
0.1%
160 1
 
0.1%
150 3
0.4%
144 1
 
0.1%

japanese_name
Text

UNIQUE 

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-09-20T15:44:32.175774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length33
Median length31
Mean length12.270911
Min length4

Characters and Unicode

Total characters9829
Distinct characters146
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique801 ?
Unique (%)100.0%

Sample

1st rowFushigidaneフシギダネ
2nd rowFushigisouフシギソウ
3rd rowFushigibanaフシギバナ
4th rowHitokageヒトカゲ
5th rowLizardoリザード
ValueCountFrequency (%)
no 4
 
0.5%
keshin 3
 
0.4%
fushigidaneフシギダネ 1
 
0.1%
golbatゴルバット 1
 
0.1%
kusaihanaクサイハナ 1
 
0.1%
ruffresiaラフレシア 1
 
0.1%
parasパラス 1
 
0.1%
parasectパラセクト 1
 
0.1%
kongpangコンパン 1
 
0.1%
sandサンド 1
 
0.1%
Other values (817) 817
98.2%
2023-09-20T15:44:32.422437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 721
 
7.3%
o 570
 
5.8%
i 473
 
4.8%
r 427
 
4.3%
e 425
 
4.3%
u 383
 
3.9%
n 338
 
3.4%
263
 
2.7%
215
 
2.2%
s 211
 
2.1%
Other values (136) 5803
59.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5268
53.6%
Other Letter 3401
34.6%
Uppercase Letter 820
 
8.3%
Modifier Letter 263
 
2.7%
Space Separator 31
 
0.3%
Open Punctuation 13
 
0.1%
Close Punctuation 13
 
0.1%
Other Punctuation 9
 
0.1%
Dash Punctuation 5
 
0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
215
 
6.3%
177
 
5.2%
133
 
3.9%
113
 
3.3%
105
 
3.1%
102
 
3.0%
95
 
2.8%
94
 
2.8%
90
 
2.6%
89
 
2.6%
Other values (68) 2188
64.3%
Lowercase Letter
ValueCountFrequency (%)
a 721
13.7%
o 570
10.8%
i 473
 
9.0%
r 427
 
8.1%
e 425
 
8.1%
u 383
 
7.3%
n 338
 
6.4%
s 211
 
4.0%
m 194
 
3.7%
k 194
 
3.7%
Other values (16) 1332
25.3%
Uppercase Letter
ValueCountFrequency (%)
M 80
 
9.8%
K 79
 
9.6%
G 52
 
6.3%
D 52
 
6.3%
N 51
 
6.2%
S 50
 
6.1%
B 44
 
5.4%
H 44
 
5.4%
P 43
 
5.2%
T 38
 
4.6%
Other values (16) 287
35.0%
Other Punctuation
ValueCountFrequency (%)
4
44.4%
? 2
22.2%
1
 
11.1%
: 1
 
11.1%
% 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
0 1
25.0%
1 1
25.0%
1
25.0%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Modifier Letter
ValueCountFrequency (%)
263
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6088
61.9%
Katakana 3401
34.6%
Common 340
 
3.5%

Most frequent character per script

Katakana
ValueCountFrequency (%)
215
 
6.3%
177
 
5.2%
133
 
3.9%
113
 
3.3%
105
 
3.1%
102
 
3.0%
95
 
2.8%
94
 
2.8%
90
 
2.6%
89
 
2.6%
Other values (68) 2188
64.3%
Latin
ValueCountFrequency (%)
a 721
 
11.8%
o 570
 
9.4%
i 473
 
7.8%
r 427
 
7.0%
e 425
 
7.0%
u 383
 
6.3%
n 338
 
5.6%
s 211
 
3.5%
m 194
 
3.2%
k 194
 
3.2%
Other values (42) 2152
35.3%
Common
ValueCountFrequency (%)
263
77.4%
31
 
9.1%
( 13
 
3.8%
) 13
 
3.8%
- 5
 
1.5%
4
 
1.2%
? 2
 
0.6%
2 1
 
0.3%
1
 
0.3%
1
 
0.3%
Other values (6) 6
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6156
62.6%
Katakana 3668
37.3%
None 3
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 721
 
11.7%
o 570
 
9.3%
i 473
 
7.7%
r 427
 
6.9%
e 425
 
6.9%
u 383
 
6.2%
n 338
 
5.5%
s 211
 
3.4%
m 194
 
3.2%
k 194
 
3.2%
Other values (51) 2220
36.1%
Katakana
ValueCountFrequency (%)
263
 
7.2%
215
 
5.9%
177
 
4.8%
133
 
3.6%
113
 
3.1%
105
 
2.9%
102
 
2.8%
95
 
2.6%
94
 
2.6%
90
 
2.5%
Other values (70) 2281
62.2%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
None
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

name
Text

UNIQUE 

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-09-20T15:44:32.604258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.4656679
Min length3

Characters and Unicode

Total characters5980
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique801 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowCharmander
5th rowCharmeleon
ValueCountFrequency (%)
tapu 4
 
0.5%
mime 2
 
0.2%
butterfree 1
 
0.1%
arbok 1
 
0.1%
venusaur 1
 
0.1%
charmander 1
 
0.1%
charmeleon 1
 
0.1%
charizard 1
 
0.1%
squirtle 1
 
0.1%
wartortle 1
 
0.1%
Other values (794) 794
98.3%
2023-09-20T15:44:32.853803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 547
 
9.1%
e 502
 
8.4%
o 490
 
8.2%
i 436
 
7.3%
r 433
 
7.2%
l 347
 
5.8%
n 346
 
5.8%
t 273
 
4.6%
u 248
 
4.1%
s 195
 
3.3%
Other values (51) 2163
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5151
86.1%
Uppercase Letter 810
 
13.5%
Space Separator 7
 
0.1%
Dash Punctuation 5
 
0.1%
Other Punctuation 4
 
0.1%
Other Symbol 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 547
10.6%
e 502
 
9.7%
o 490
 
9.5%
i 436
 
8.5%
r 433
 
8.4%
l 347
 
6.7%
n 346
 
6.7%
t 273
 
5.3%
u 248
 
4.8%
s 195
 
3.8%
Other values (17) 1334
25.9%
Uppercase Letter
ValueCountFrequency (%)
S 111
13.7%
M 67
 
8.3%
C 63
 
7.8%
P 55
 
6.8%
G 51
 
6.3%
T 49
 
6.0%
D 46
 
5.7%
B 45
 
5.6%
L 39
 
4.8%
A 34
 
4.2%
Other values (16) 250
30.9%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
: 1
25.0%
' 1
25.0%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5961
99.7%
Common 19
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 547
 
9.2%
e 502
 
8.4%
o 490
 
8.2%
i 436
 
7.3%
r 433
 
7.3%
l 347
 
5.8%
n 346
 
5.8%
t 273
 
4.6%
u 248
 
4.2%
s 195
 
3.3%
Other values (43) 2144
36.0%
Common
ValueCountFrequency (%)
7
36.8%
- 5
26.3%
. 2
 
10.5%
: 1
 
5.3%
1
 
5.3%
1
 
5.3%
' 1
 
5.3%
2 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5976
99.9%
None 2
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 547
 
9.2%
e 502
 
8.4%
o 490
 
8.2%
i 436
 
7.3%
r 433
 
7.2%
l 347
 
5.8%
n 346
 
5.8%
t 273
 
4.6%
u 248
 
4.1%
s 195
 
3.3%
Other values (48) 2159
36.1%
None
ValueCountFrequency (%)
é 2
100.0%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%

percentage_male
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)1.0%
Missing98
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean55.155761
Minimum0
Maximum100
Zeros27
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:32.931340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.6
Q150
median50
Q350
95-th percentile88.099998
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.261623
Coefficient of variation (CV)0.3673528
Kurtosis1.1591005
Mean55.155761
Median Absolute Deviation (MAD)0
Skewness0.066346623
Sum38774.5
Variance410.53336
MonotonicityNot monotonic
2023-09-20T15:44:32.988728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
50 501
62.5%
88.09999847 111
 
13.9%
0 27
 
3.4%
24.60000038 24
 
3.0%
100 19
 
2.4%
75.40000153 19
 
2.4%
11.19999981 2
 
0.2%
(Missing) 98
 
12.2%
ValueCountFrequency (%)
0 27
 
3.4%
11.19999981 2
 
0.2%
24.60000038 24
 
3.0%
50 501
62.5%
75.40000153 19
 
2.4%
88.09999847 111
 
13.9%
100 19
 
2.4%
ValueCountFrequency (%)
100 19
 
2.4%
88.09999847 111
 
13.9%
75.40000153 19
 
2.4%
50 501
62.5%
24.60000038 24
 
3.0%
11.19999981 2
 
0.2%
0 27
 
3.4%

pokedex_number
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401
Minimum1
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:33.065950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1201
median401
Q3601
95-th percentile761
Maximum801
Range800
Interquartile range (IQR)400

Descriptive statistics

Standard deviation231.37308
Coefficient of variation (CV)0.57699021
Kurtosis-1.2
Mean401
Median Absolute Deviation (MAD)200
Skewness0
Sum321201
Variance53533.5
MonotonicityStrictly increasing
2023-09-20T15:44:33.156043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
539 1
 
0.1%
529 1
 
0.1%
530 1
 
0.1%
531 1
 
0.1%
532 1
 
0.1%
533 1
 
0.1%
534 1
 
0.1%
535 1
 
0.1%
536 1
 
0.1%
Other values (791) 791
98.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
801 1
0.1%
800 1
0.1%
799 1
0.1%
798 1
0.1%
797 1
0.1%
796 1
0.1%
795 1
0.1%
794 1
0.1%
793 1
0.1%
792 1
0.1%

sp_attack
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.305868
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:33.252681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q145
median65
Q391
95-th percentile131
Maximum194
Range184
Interquartile range (IQR)46

Descriptive statistics

Standard deviation32.353826
Coefficient of variation (CV)0.45373302
Kurtosis0.41248641
Mean71.305868
Median Absolute Deviation (MAD)21
Skewness0.77837077
Sum57116
Variance1046.7701
MonotonicityNot monotonic
2023-09-20T15:44:33.339352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 52
 
6.5%
60 48
 
6.0%
65 43
 
5.4%
50 42
 
5.2%
55 38
 
4.7%
45 34
 
4.2%
70 30
 
3.7%
95 30
 
3.7%
35 29
 
3.6%
30 28
 
3.5%
Other values (101) 427
53.3%
ValueCountFrequency (%)
10 4
 
0.5%
15 4
 
0.5%
20 8
 
1.0%
23 1
 
0.1%
24 2
 
0.2%
25 12
1.5%
27 2
 
0.2%
29 3
 
0.4%
30 28
3.5%
31 1
 
0.1%
ValueCountFrequency (%)
194 1
 
0.1%
180 2
 
0.2%
175 1
 
0.1%
173 1
 
0.1%
170 3
0.4%
165 2
 
0.2%
160 2
 
0.2%
159 1
 
0.1%
153 1
 
0.1%
150 5
0.6%

sp_defense
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.911361
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:33.433766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31
Q150
median66
Q390
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.942501
Coefficient of variation (CV)0.3940483
Kurtosis1.525919
Mean70.911361
Median Absolute Deviation (MAD)19
Skewness0.86762026
Sum56800
Variance780.78338
MonotonicityNot monotonic
2023-09-20T15:44:33.517803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 53
 
6.6%
60 44
 
5.5%
65 42
 
5.2%
55 42
 
5.2%
70 41
 
5.1%
80 41
 
5.1%
75 39
 
4.9%
45 38
 
4.7%
40 33
 
4.1%
90 32
 
4.0%
Other values (87) 396
49.4%
ValueCountFrequency (%)
20 5
 
0.6%
23 1
 
0.1%
25 11
1.4%
30 22
2.7%
31 3
 
0.4%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
35 20
2.5%
36 1
 
0.1%
ValueCountFrequency (%)
230 1
 
0.1%
200 1
 
0.1%
160 1
 
0.1%
154 3
0.4%
150 5
0.6%
142 1
 
0.1%
140 1
 
0.1%
138 1
 
0.1%
135 5
0.6%
132 1
 
0.1%

speed
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.334582
Minimum5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-20T15:44:33.611493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q385
95-th percentile115
Maximum180
Range175
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.907662
Coefficient of variation (CV)0.4357857
Kurtosis-0.11866801
Mean66.334582
Median Absolute Deviation (MAD)20
Skewness0.43891818
Sum53134
Variance835.65292
MonotonicityNot monotonic
2023-09-20T15:44:33.696956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 46
 
5.7%
50 43
 
5.4%
65 38
 
4.7%
45 36
 
4.5%
70 36
 
4.5%
30 35
 
4.4%
40 34
 
4.2%
80 29
 
3.6%
55 28
 
3.5%
85 28
 
3.5%
Other values (103) 448
55.9%
ValueCountFrequency (%)
5 3
 
0.4%
10 3
 
0.4%
15 11
1.4%
20 15
1.9%
22 1
 
0.1%
23 4
 
0.5%
24 1
 
0.1%
25 10
1.2%
27 1
 
0.1%
28 4
 
0.5%
ValueCountFrequency (%)
180 1
 
0.1%
160 1
 
0.1%
151 1
 
0.1%
150 3
0.4%
145 3
0.4%
140 1
 
0.1%
135 2
0.2%
132 1
 
0.1%
130 4
0.5%
128 1
 
0.1%

type1
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
water
114 
normal
105 
grass
78 
bug
72 
psychic
53 
Other values (13)
379 

Length

Max length8
Median length7
Mean length5.2372035
Min length3

Characters and Unicode

Total characters4195
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowfire
5th rowfire

Common Values

ValueCountFrequency (%)
water 114
14.2%
normal 105
13.1%
grass 78
9.7%
bug 72
9.0%
psychic 53
 
6.6%
fire 52
 
6.5%
rock 45
 
5.6%
electric 39
 
4.9%
ground 32
 
4.0%
poison 32
 
4.0%
Other values (8) 179
22.3%

Length

2023-09-20T15:44:33.779525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water 114
14.2%
normal 105
13.1%
grass 78
9.7%
bug 72
9.0%
psychic 53
 
6.6%
fire 52
 
6.5%
rock 45
 
5.6%
electric 39
 
4.9%
poison 32
 
4.0%
ground 32
 
4.0%
Other values (8) 179
22.3%

Most occurring characters

ValueCountFrequency (%)
r 539
12.8%
a 371
 
8.8%
e 315
 
7.5%
o 300
 
7.2%
g 295
 
7.0%
s 292
 
7.0%
i 276
 
6.6%
c 252
 
6.0%
t 232
 
5.5%
n 227
 
5.4%
Other values (11) 1096
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4195
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 539
12.8%
a 371
 
8.8%
e 315
 
7.5%
o 300
 
7.2%
g 295
 
7.0%
s 292
 
7.0%
i 276
 
6.6%
c 252
 
6.0%
t 232
 
5.5%
n 227
 
5.4%
Other values (11) 1096
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 539
12.8%
a 371
 
8.8%
e 315
 
7.5%
o 300
 
7.2%
g 295
 
7.0%
s 292
 
7.0%
i 276
 
6.6%
c 252
 
6.0%
t 232
 
5.5%
n 227
 
5.4%
Other values (11) 1096
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 539
12.8%
a 371
 
8.8%
e 315
 
7.5%
o 300
 
7.2%
g 295
 
7.0%
s 292
 
7.0%
i 276
 
6.6%
c 252
 
6.0%
t 232
 
5.5%
n 227
 
5.4%
Other values (11) 1096
26.1%

type2
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)4.3%
Missing384
Missing (%)47.9%
Memory size6.4 KiB
flying
95 
poison
34 
ground
34 
psychic
29 
fairy
29 
Other values (13)
196 

Length

Max length8
Median length7
Mean length5.6139089
Min length3

Characters and Unicode

Total characters2341
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpoison
2nd rowpoison
3rd rowpoison
4th rowflying
5th rowflying

Common Values

ValueCountFrequency (%)
flying 95
 
11.9%
poison 34
 
4.2%
ground 34
 
4.2%
psychic 29
 
3.6%
fairy 29
 
3.6%
fighting 25
 
3.1%
steel 22
 
2.7%
dark 21
 
2.6%
grass 20
 
2.5%
water 17
 
2.1%
Other values (8) 91
 
11.4%
(Missing) 384
47.9%

Length

2023-09-20T15:44:33.849300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying 95
22.8%
ground 34
 
8.2%
poison 34
 
8.2%
psychic 29
 
7.0%
fairy 29
 
7.0%
fighting 25
 
6.0%
steel 22
 
5.3%
dark 21
 
5.0%
grass 20
 
4.8%
dragon 17
 
4.1%
Other values (8) 91
21.8%

Most occurring characters

ValueCountFrequency (%)
i 274
11.7%
g 235
 
10.0%
n 209
 
8.9%
r 178
 
7.6%
f 162
 
6.9%
y 153
 
6.5%
o 151
 
6.5%
s 139
 
5.9%
l 130
 
5.6%
a 108
 
4.6%
Other values (11) 602
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2341
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 274
11.7%
g 235
 
10.0%
n 209
 
8.9%
r 178
 
7.6%
f 162
 
6.9%
y 153
 
6.5%
o 151
 
6.5%
s 139
 
5.9%
l 130
 
5.6%
a 108
 
4.6%
Other values (11) 602
25.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2341
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 274
11.7%
g 235
 
10.0%
n 209
 
8.9%
r 178
 
7.6%
f 162
 
6.9%
y 153
 
6.5%
o 151
 
6.5%
s 139
 
5.9%
l 130
 
5.6%
a 108
 
4.6%
Other values (11) 602
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 274
11.7%
g 235
 
10.0%
n 209
 
8.9%
r 178
 
7.6%
f 162
 
6.9%
y 153
 
6.5%
o 151
 
6.5%
s 139
 
5.9%
l 130
 
5.6%
a 108
 
4.6%
Other values (11) 602
25.7%

weight_kg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct421
Distinct (%)53.9%
Missing20
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean61.378105
Minimum0.1
Maximum999.90002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-09-20T15:44:33.930111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.5
Q19
median27.299999
Q364.800003
95-th percentile230
Maximum999.90002
Range999.80002
Interquartile range (IQR)55.800003

Descriptive statistics

Standard deviation109.35477
Coefficient of variation (CV)1.7816576
Kurtosis31.735823
Mean61.378105
Median Absolute Deviation (MAD)21.299999
Skewness4.8710446
Sum47936.3
Variance11958.466
MonotonicityNot monotonic
2023-09-20T15:44:34.019371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 9
 
1.1%
8.5 8
 
1.0%
5 8
 
1.0%
28 8
 
1.0%
0.3000000119 7
 
0.9%
1 7
 
0.9%
4 7
 
0.9%
2 7
 
0.9%
12 6
 
0.7%
1.200000048 6
 
0.7%
Other values (411) 708
88.4%
(Missing) 20
 
2.5%
ValueCountFrequency (%)
0.1000000015 5
0.6%
0.200000003 1
 
0.1%
0.3000000119 7
0.9%
0.5 3
0.4%
0.6000000238 3
0.4%
0.6999999881 1
 
0.1%
0.8000000119 1
 
0.1%
0.8999999762 1
 
0.1%
1 7
0.9%
1.100000024 1
 
0.1%
ValueCountFrequency (%)
999.9000244 2
0.2%
950 1
0.1%
920 1
0.1%
888 1
0.1%
750 1
0.1%
683 1
0.1%
550 1
0.1%
505 1
0.1%
460 1
0.1%
430 1
0.1%

generation
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.690387
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size929.0 B
2023-09-20T15:44:34.096524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9304196
Coefficient of variation (CV)0.52309409
Kurtosis-1.11901
Mean3.690387
Median Absolute Deviation (MAD)2
Skewness0.11720718
Sum2956
Variance3.72652
MonotonicityIncreasing
2023-09-20T15:44:34.152692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 156
19.5%
1 151
18.9%
3 135
16.9%
4 107
13.4%
2 100
12.5%
7 80
10.0%
6 72
9.0%
ValueCountFrequency (%)
1 151
18.9%
2 100
12.5%
3 135
16.9%
4 107
13.4%
5 156
19.5%
6 72
9.0%
7 80
10.0%
ValueCountFrequency (%)
7 80
10.0%
6 72
9.0%
5 156
19.5%
4 107
13.4%
3 135
16.9%
2 100
12.5%
1 151
18.9%

is_legendary
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
731 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters801
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Length

2023-09-20T15:44:34.221348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-20T15:44:34.293224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 801
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 801
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 731
91.3%
1 70
 
8.7%

Interactions

2023-09-20T15:44:23.766380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:49.365616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.548461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.436503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.278861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.302524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:59.083122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:01.035719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.713742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.423880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.403536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:08.001361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.670141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.693042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.324229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.910973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.655877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.852881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.414249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:22.082621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.855781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:49.458943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.642689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.528001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.372140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.401338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:59.170757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:01.123796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.801734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.783693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.490546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:08.090390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.757383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.781418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.408389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:15.001908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.743525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.935217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.503317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:22.169415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.944591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:49.548973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.735283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.619079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.468095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.500914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:59.259444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:01.211780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.889625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.870855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.572714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:08.177866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.844389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.872669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.491509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:15.095187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.832908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:19.016548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.590414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:22.256618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:24.032469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:49.640753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.825763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.711910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.563452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.594278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:59.348954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:01.301430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.980912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.961138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.656058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:08.265725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.931580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.957992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.577819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:15.187724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.927618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:19.101486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.678289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:22.341423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:24.121910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:50.119203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.918677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.806856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.656035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.691534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:59.435274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:01.389548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:03.069849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:05.048529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.740290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-20T15:44:11.184284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:12.844405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.444925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.141873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.347234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:19.955048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:21.591138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.278900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:25.064961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.117505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.010788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:54.805335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:56.831211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:58.664055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:00.603722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.306413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.008301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:05.983587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:07.608579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.266371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.270926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:12.927792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.529331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.231679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.435045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.037181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:21.679108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.367358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:25.157084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.207875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.103065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:54.898619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:56.925101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:58.750258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:00.703748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.390199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.092993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.073573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:07.690596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.350486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.357066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.008051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.607415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.320472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.521263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.117801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:21.764732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.451621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:25.231456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.291361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.181563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:54.984313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.006656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:58.829173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:00.783612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.466707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.173568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.149769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:07.760947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.426397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.437012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.090350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.678484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.399224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.598236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.188140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:21.839602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.531064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:25.334924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.379000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.268558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.083971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.121399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:58.917378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:00.868346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.553725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.259595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.235924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:07.841666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.510888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.524737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.170960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.759853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.487669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.691832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.266732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:21.921669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.611541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:25.427762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:51.461830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:53.350708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:55.182709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:57.208391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:43:58.999260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:00.952580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:02.633543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:04.339097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:06.321367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:07.923521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:09.588045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:11.607984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:13.245560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:14.834122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:16.569849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:18.770873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:20.339585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:22.000215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-20T15:44:23.688381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-09-20T15:44:34.387139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
against_electricagainst_fightagainst_groundagainst_poisonagainst_psychicattackbase_egg_stepsbase_happinessbase_totaldefenseexperience_growthheight_mhppercentage_malepokedex_numbersp_attacksp_defensespeedweight_kggenerationagainst_bugagainst_darkagainst_dragonagainst_fairyagainst_fireagainst_flyingagainst_ghostagainst_grassagainst_iceagainst_normalagainst_rockagainst_steelagainst_watercapture_ratetype1type2is_legendary
against_electric1.000-0.091-0.1960.040-0.021-0.095-0.1290.024-0.014-0.077-0.083-0.032-0.0280.047-0.0760.017-0.0130.120-0.074-0.0710.2070.1140.1860.1180.2670.2190.1340.3850.4110.0530.3370.1610.4080.0630.6060.5590.000
against_fight-0.0911.0000.309-0.087-0.1700.1830.082-0.0920.0460.0780.0680.0840.1150.0760.004-0.117-0.089-0.0280.255-0.0060.2140.2480.2420.1200.1780.3120.3820.2580.2190.5860.2010.2380.1890.1190.5450.4470.086
against_ground-0.1960.3091.000-0.4810.0020.0560.183-0.0100.0740.1220.0130.042-0.0050.082-0.0290.0730.060-0.0730.156-0.0440.3060.0670.2710.3370.3260.6110.1470.3860.3320.4400.3430.2990.2820.0000.5380.5830.000
against_poison0.040-0.087-0.4811.0000.031-0.168-0.1880.093-0.083-0.2450.015-0.1260.003-0.0160.0280.091-0.0060.061-0.2390.0560.2500.1540.5670.2390.2610.3470.1500.2140.1940.4560.3010.2400.2840.0640.5890.5170.080
against_psychic-0.021-0.1700.0020.0311.000-0.012-0.1350.162-0.127-0.129-0.0420.031-0.0370.114-0.127-0.151-0.126-0.043-0.068-0.0990.3260.4120.2720.3710.1250.2460.5370.1710.0890.1730.1570.1250.0820.1670.6190.5890.211
attack-0.0950.1830.056-0.168-0.0121.0000.288-0.1820.7180.5310.2580.5910.5610.1840.1370.3500.3220.3470.5550.1000.0230.1060.1440.1330.0800.0960.1210.0550.1200.1100.1210.0370.0550.1690.1120.0420.314
base_egg_steps-0.1290.0820.183-0.188-0.1350.2881.000-0.3570.3970.2670.3070.3050.2430.2000.0940.3130.3080.1910.3390.0030.0580.1240.3010.1690.1830.1740.1620.1130.1160.2480.1330.1230.1320.3940.3820.2270.927
base_happiness0.024-0.092-0.0100.0930.162-0.182-0.3571.000-0.182-0.149-0.303-0.237-0.0730.008-0.106-0.129-0.107-0.099-0.256-0.0620.0310.1440.2030.1730.0670.0830.2090.0950.1240.0800.0000.0720.0140.2840.2910.0930.785
base_total-0.0140.0460.074-0.083-0.1270.7180.397-0.1821.0000.6950.2730.7110.7270.1230.1460.7250.7550.5330.6350.0910.0370.1430.1910.0660.0780.1020.1310.0000.1070.0700.1050.0410.0390.3720.1280.0160.784
defense-0.0770.0780.122-0.245-0.1290.5310.267-0.1490.6951.0000.1630.5000.4450.0560.1270.3230.5950.0720.5370.0750.0000.0000.2010.0000.1240.1680.0670.0880.0920.2890.1740.0640.0950.2350.1400.1600.327
experience_growth-0.0830.0680.0130.015-0.0420.2580.307-0.3030.2730.1631.0000.2340.2140.1560.0720.2150.1100.1760.2460.0450.0750.0950.2460.1180.1120.1330.1080.1140.1170.1300.0530.0960.1220.3740.2840.2150.555
height_m-0.0320.0840.042-0.1260.0310.5910.305-0.2370.7110.5000.2341.0000.6310.107-0.0440.4720.5080.3280.837-0.0840.0000.1000.1710.0650.0460.0490.1140.0280.0700.0750.1290.0000.1020.1970.0900.1080.467
hp-0.0280.115-0.0050.003-0.0370.5610.243-0.0730.7270.4450.2140.6311.0000.0020.1260.4850.4980.2520.5960.0940.0000.0890.0910.0930.0000.0000.1230.0180.0800.0000.0000.0000.0000.2780.0400.0350.360
percentage_male0.0470.0760.082-0.0160.1140.1840.2000.0080.1230.0560.1560.1070.0021.000-0.0360.1070.0520.0670.129-0.0100.1290.1110.1640.1170.1690.0570.1480.1280.1640.0920.1000.1380.1500.3850.3020.3290.353
pokedex_number-0.0760.004-0.0290.028-0.1270.1370.094-0.1060.1460.1270.072-0.0440.126-0.0361.0000.1040.0910.0060.0170.9870.0830.1100.1790.1320.0740.1040.0700.0960.1000.1230.0000.0720.0720.1660.1330.2180.227
sp_attack0.017-0.1170.0730.091-0.1510.3500.313-0.1290.7250.3230.2150.4720.4850.1070.1041.0000.5700.4310.3500.0660.0690.1330.0720.0000.0710.0000.1460.0000.1090.0000.0910.1310.0650.1940.1360.0470.437
sp_defense-0.013-0.0890.060-0.006-0.1260.3220.308-0.1070.7550.5950.1100.5080.4980.0520.0910.5701.0000.2780.4620.0440.0000.0820.1160.0000.0000.0300.0970.0750.0810.1010.0270.0700.0000.2210.0860.0660.366
speed0.120-0.028-0.0730.061-0.0430.3470.191-0.0990.5330.0720.1760.3280.2520.0670.0060.4310.2781.0000.184-0.0160.0580.0390.0460.0150.0440.0560.0910.0610.0640.0950.0610.0730.0350.1510.1080.1200.337
weight_kg-0.0740.2550.156-0.239-0.0680.5550.339-0.2560.6350.5370.2460.8370.5960.1290.0170.3500.4620.1841.000-0.0280.0000.0000.1880.2270.0580.0910.0780.0000.1050.1900.0850.0980.1020.2610.1260.1040.406
generation-0.071-0.006-0.0440.056-0.0990.1000.003-0.0620.0910.0750.045-0.0840.094-0.0100.9870.0660.044-0.016-0.0281.0000.0670.0770.1500.1160.0500.0210.0570.0450.0000.0760.0000.1030.0460.1700.1200.2540.150
against_bug0.2070.2140.3060.2500.3260.0230.0580.0310.0370.0000.0750.0000.0000.1290.0830.0690.0000.0580.0000.0671.0000.2420.1660.2800.1190.1550.2360.2260.1200.1830.2270.1320.1830.0890.4470.4290.051
against_dark0.1140.2480.0670.1540.4120.1060.1240.1440.1430.0000.0950.1000.0890.1110.1100.1330.0820.0390.0000.0770.2421.0000.2710.3810.0990.1190.7390.1320.0650.3200.1440.1190.0800.1970.5010.5180.211
against_dragon0.1860.2420.2710.5670.2720.1440.3010.2030.1910.2010.2460.1710.0910.1640.1790.0720.1160.0460.1880.1500.1660.2711.0000.4090.2970.3270.0700.1550.3000.3640.2810.3610.1640.2330.6810.7980.156
against_fairy0.1180.1200.3370.2390.3710.1330.1690.1730.0660.0000.1180.0650.0930.1170.1320.0000.0000.0150.2270.1160.2800.3810.4091.0000.1260.1460.2770.2500.2070.1440.1840.2040.1630.1430.5350.4600.070
against_fire0.2670.1780.3260.2610.1250.0800.1830.0670.0780.1240.1120.0460.0000.1690.0740.0710.0000.0440.0580.0500.1190.0990.2970.1261.0000.3970.2360.4670.3210.2190.1290.2810.3650.0980.6160.4330.000
against_flying0.2190.3120.6110.3470.2460.0960.1740.0830.1020.1680.1330.0490.0000.0570.1040.0000.0300.0560.0910.0210.1550.1190.3270.1460.3971.0000.1660.2620.1840.5840.1910.3060.2080.0640.6020.4440.140
against_ghost0.1340.3820.1470.1500.5370.1210.1620.2090.1310.0670.1080.1140.1230.1480.0700.1460.0970.0910.0780.0570.2360.7390.0700.2770.2360.1661.0000.1930.1380.3300.0940.1600.1850.1660.7280.4950.199
against_grass0.3850.2580.3860.2140.1710.0550.1130.0950.0000.0880.1140.0280.0180.1280.0960.0000.0750.0610.0000.0450.2260.1320.1550.2500.4670.2620.1931.0000.3640.2110.3220.1970.3870.0630.5680.4550.014
against_ice0.4110.2190.3320.1940.0890.1200.1160.1240.1070.0920.1170.0700.0800.1640.1000.1090.0810.0640.1050.0000.1200.0650.3000.2070.3210.1840.1380.3641.0000.1110.1430.3690.2960.0000.5110.4090.045
against_normal0.0530.5860.4400.4560.1730.1100.2480.0800.0700.2890.1300.0750.0000.0920.1230.0000.1010.0950.1900.0760.1830.3200.3640.1440.2190.5840.3300.2110.1111.0000.2080.2550.2330.0000.6510.5390.065
against_rock0.3370.2010.3430.3010.1570.1210.1330.0000.1050.1740.0530.1290.0000.1000.0000.0910.0270.0610.0850.0000.2270.1440.2810.1840.1290.1910.0940.3220.1430.2081.0000.0690.1980.1230.4720.4610.006
against_steel0.1610.2380.2990.2400.1250.0370.1230.0720.0410.0640.0960.0000.0000.1380.0720.1310.0700.0730.0980.1030.1320.1190.3610.2040.2810.3060.1600.1970.3690.2550.0691.0000.2250.0000.5600.3930.016
against_water0.4080.1890.2820.2840.0820.0550.1320.0140.0390.0950.1220.1020.0000.1500.0720.0650.0000.0350.1020.0460.1830.0800.1640.1630.3650.2080.1850.3870.2960.2330.1980.2251.0000.0000.5830.3750.000
capture_rate0.0630.1190.0000.0640.1670.1690.3940.2840.3720.2350.3740.1970.2780.3850.1660.1940.2210.1510.2610.1700.0890.1970.2330.1430.0980.0640.1660.0630.0000.0000.1230.0000.0001.0000.1500.1680.802
type10.6060.5450.5380.5890.6190.1120.3820.2910.1280.1400.2840.0900.0400.3020.1330.1360.0860.1080.1260.1200.4470.5010.6810.5350.6160.6020.7280.5680.5110.6510.4720.5600.5830.1501.0000.2090.267
type20.5590.4470.5830.5170.5890.0420.2270.0930.0160.1600.2150.1080.0350.3290.2180.0470.0660.1200.1040.2540.4290.5180.7980.4600.4330.4440.4950.4550.4090.5390.4610.3930.3750.1680.2091.0000.117
is_legendary0.0000.0860.0000.0800.2110.3140.9270.7850.7840.3270.5550.4670.3600.3530.2270.4370.3660.3370.4060.1500.0510.2110.1560.0700.0000.1400.1990.0140.0450.0650.0060.0160.0000.8020.2670.1171.000

Missing values

2023-09-20T15:44:25.592349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-20T15:44:26.210413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-20T15:44:26.424819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

abilitiesagainst_bugagainst_darkagainst_dragonagainst_electricagainst_fairyagainst_fightagainst_fireagainst_flyingagainst_ghostagainst_grassagainst_groundagainst_iceagainst_normalagainst_poisonagainst_psychicagainst_rockagainst_steelagainst_waterattackbase_egg_stepsbase_happinessbase_totalcapture_rateclassficationdefenseexperience_growthheight_mhpjapanese_namenamepercentage_malepokedex_numbersp_attacksp_defensespeedtype1type2weight_kggenerationis_legendary
0['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.54951207031845Seed Pokémon4910598600.745FushigidaneフシギダネBulbasaur88.0999981656545grasspoison6.910
1['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.56251207040545Seed Pokémon6310598601.060FushigisouフシギソウIvysaur88.0999982808060grasspoison13.010
2['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.510051207062545Seed Pokémon12310598602.080FushigibanaフシギバナVenusaur88.099998312212080grasspoison100.010
3['Blaze', 'Solar Power']0.501.01.01.00.51.00.51.01.00.502.00.51.01.01.02.00.52.05251207030945Lizard Pokémon4310598600.639HitokageヒトカゲCharmander88.0999984605065fireNaN8.510
4['Blaze', 'Solar Power']0.501.01.01.00.51.00.51.01.00.502.00.51.01.01.02.00.52.06451207040545Flame Pokémon5810598601.158LizardoリザードCharmeleon88.0999985806580fireNaN19.010
5['Blaze', 'Solar Power']0.251.01.02.00.50.50.51.01.00.250.01.01.01.01.04.00.52.010451207063445Flame Pokémon7810598601.778LizardonリザードンCharizard88.0999986159115100fireflying90.510
6['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.54851207031445Tiny Turtle Pokémon6510598600.544ZenigameゼニガメSquirtle88.0999987506443waterNaN9.010
7['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.56351207040545Turtle Pokémon8010598601.059KameilカメールWartortle88.0999988658058waterNaN22.510
8['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.510351207063045Shellfish Pokémon12010598601.679KamexカメックスBlastoise88.099998913511578waterNaN85.510
9['Shield Dust', 'Run Away']1.001.01.01.01.00.52.02.01.00.500.51.01.01.01.02.01.01.030384070195255Worm Pokémon3510000000.345CaterpieキャタピーCaterpie50.00000010202045bugNaN2.910
abilitiesagainst_bugagainst_darkagainst_dragonagainst_electricagainst_fairyagainst_fightagainst_fireagainst_flyingagainst_ghostagainst_grassagainst_groundagainst_iceagainst_normalagainst_poisonagainst_psychicagainst_rockagainst_steelagainst_waterattackbase_egg_stepsbase_happinessbase_totalcapture_rateclassficationdefenseexperience_growthheight_mhpjapanese_namenamepercentage_malepokedex_numbersp_attacksp_defensespeedtype1type2weight_kggenerationis_legendary
791['Shadow Shield']1.004.01.01.01.00.01.01.04.01.001.01.00.00.500.51.01.01.011330720068045Moone Pokémon8912500004.0137LunalaルナアーラLunalaNaN79213710797psychicghost120.00000071
792['Beast Boost']0.501.01.01.00.51.00.50.51.01.004.01.00.50.252.01.02.02.05330720057045Parasite Pokémon4712500001.2109UturoidウツロイドNihilegoNaN793127131103rockpoison55.50000071
793['Beast Boost']0.500.51.01.02.00.52.04.01.00.500.51.01.01.002.01.01.01.013930720057025Swollen Pokémon13912500002.4107MassivoonマッシブーンBuzzwoleNaN794535379bugfighting333.60000671
794['Beast Boost']0.500.51.01.02.00.52.04.01.00.500.51.01.01.002.01.01.01.0137307200570255Lissome Pokémon3712500001.871PheroacheフェローチェPheromosaNaN79513737151bugfighting25.00000071
795['Beast Boost']1.001.01.00.51.01.01.00.51.01.002.01.01.01.001.01.00.51.08930720057030Glowing Pokémon7112500003.883DenjyumokuデンジュモクXurkitreeNaN7961737183electricNaN100.00000071
796['Beast Boost']0.251.00.52.00.51.02.00.51.00.250.01.00.50.000.51.00.51.010130720057025Launch Pokémon10312500009.297TekkaguyaテッカグヤCelesteelaNaN79710710161steelflying999.90002471
797['Beast Boost']1.001.00.50.50.52.04.01.01.00.251.01.00.50.000.50.50.50.5181307200570255Drawn Sword Pokémon13112500000.359KamiturugiカミツルギKartanaNaN7985931109grasssteel0.10000071
798['Beast Boost']2.000.52.00.54.02.00.51.00.50.501.02.01.01.000.01.01.00.510130720057015Junkivore Pokémon5312500005.5223AkuzikingアクジキングGuzzlordNaN799975343darkdragon888.00000071
799['Prism Armor']2.002.01.01.01.00.51.01.02.01.001.01.01.01.000.51.01.01.01073072006003Prism Pokémon10112500002.497NecrozmaネクロズマNecrozmaNaN8001278979psychicNaN230.00000071
800['Soul-Heart']0.250.50.01.00.51.02.00.51.00.502.00.50.50.000.50.51.01.0953072006003Artificial Pokémon11512500001.080MagearnaマギアナMagearnaNaN80113011565steelfairy80.50000071